Overview

Dataset statistics

Number of variables17
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory138.6 B

Variable types

NUM12
CAT4
BOOL1

Warnings

track_id is highly correlated with albumHigh correlation
album is highly correlated with track_id and 1 other fieldsHigh correlation
artist is highly correlated with albumHigh correlation
album is uniformly distributed Uniform
artist is uniformly distributed Uniform
track_name has unique values Unique
track_id has unique values Unique
duration has unique values Unique
loudness has unique values Unique
acousticness has unique values Unique
tempo has unique values Unique
key has 4 (8.0%) zeros Zeros
instrumentalness has 11 (22.0%) zeros Zeros

Reproduction

Analysis started2020-12-02 12:36:42.018825
Analysis finished2020-12-02 12:37:03.249627
Duration21.23 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

track_name
Categorical

UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
Genesis
 
1
Full Metal
 
1
Edanna - Kora Remix
 
1
Sommer in Berlin
 
1
Who - Single Version
 
1
Other values (45)
45 
ValueCountFrequency (%) 
Genesis12.0%
 
Full Metal12.0%
 
Edanna - Kora Remix12.0%
 
Sommer in Berlin12.0%
 
Who - Single Version12.0%
 
Blue (Da Ba Dee) - Gabry Ponte Ice Pop Radio12.0%
 
Huldra - Other Version12.0%
 
Self Care12.0%
 
Hurricane 2.012.0%
 
Zucker (feat. Vanessa Mason)12.0%
 
Other values (40)4080.0%
 
2020-12-02T13:37:03.353625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique50 ?
Unique (%)100.0%
2020-12-02T13:37:03.488622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length54
Median length12.5
Mean length17.44
Min length4

album
Categorical

HIGH CORRELATION
UNIFORM

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
Evolution
 
3
Stadtaffe
 
2
Perception
 
2
Zver
 
1
Cyka Blyat
 
1
Other values (41)
41 
ValueCountFrequency (%) 
Evolution36.0%
 
Stadtaffe24.0%
 
Perception24.0%
 
Zver12.0%
 
Cyka Blyat12.0%
 
Veto12.0%
 
Area 5112.0%
 
Zone 4: Crainte / Errance - EP12.0%
 
Berlin Nights12.0%
 
Blueprints12.0%
 
Other values (36)3672.0%
 
2020-12-02T13:37:03.627582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique43 ?
Unique (%)86.0%
2020-12-02T13:37:03.778622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length45
Median length11
Mean length15.14
Min length3

track_id
Categorical

HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
5bJ1DrEM4hNCafcDd1oxHx
 
1
3ov7g9uVv0Mj3GLhOLxxa1
 
1
0z6OxbSX5sIwsSefosMmAM
 
1
3ihW8JNaU47g7uftn3nVU0
 
1
17nPeSliosCi427f0lUb75
 
1
Other values (45)
45 
ValueCountFrequency (%) 
5bJ1DrEM4hNCafcDd1oxHx12.0%
 
3ov7g9uVv0Mj3GLhOLxxa112.0%
 
0z6OxbSX5sIwsSefosMmAM12.0%
 
3ihW8JNaU47g7uftn3nVU012.0%
 
17nPeSliosCi427f0lUb7512.0%
 
5922vbc1Fz0Qd0AVC6w9It12.0%
 
2VopDw2GlF3uwD1kihHmTT12.0%
 
19Ba4C1oVJfjxGFLiPEbsJ12.0%
 
3oMueNQTlVgOdIfFP9ENz912.0%
 
0mIF9iQ8yCx2bBnROEY5Oe12.0%
 
Other values (40)4080.0%
 
2020-12-02T13:37:03.905622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique50 ?
Unique (%)100.0%
2020-12-02T13:37:04.023624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length22
Min length22

artist
Categorical

HIGH CORRELATION
UNIFORM

Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
Joyner Lucas
 
3
Peter Fox
 
2
Eiffel 65
 
2
Geegun
 
2
NF
 
2
Other values (39)
39 
ValueCountFrequency (%) 
Joyner Lucas36.0%
 
Peter Fox24.0%
 
Eiffel 6524.0%
 
Geegun24.0%
 
NF24.0%
 
The Hacker12.0%
 
Native Urbs12.0%
 
Thirty Seconds To Mars12.0%
 
Rammstein12.0%
 
Wage War12.0%
 
Other values (34)3468.0%
 
2020-12-02T13:37:04.161622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique39 ?
Unique (%)78.0%
2020-12-02T13:37:04.307623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length9
Mean length9.2
Min length2

duration
Real number (ℝ≥0)

UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277502.26
Minimum126417
Maximum772800
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:04.426621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum126417
5-th percentile150714.65
Q1202923.25
median240424
Q3292100.25
95-th percentile488627.95
Maximum772800
Range646383
Interquartile range (IQR)89177

Descriptive statistics

Standard deviation124386.6783
Coefficient of variation (CV)0.4482366316
Kurtosis4.489747151
Mean277502.26
Median Absolute Deviation (MAD)44373.5
Skewness1.903251915
Sum13875113
Variance1.547204574e+10
MonotocityNot monotonic
2020-12-02T13:37:04.552622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
25983912.0%
 
59520012.0%
 
18322312.0%
 
14891612.0%
 
25630612.0%
 
34513312.0%
 
20458612.0%
 
26400012.0%
 
48695112.0%
 
38557312.0%
 
Other values (40)4080.0%
 
ValueCountFrequency (%) 
12641712.0%
 
14343712.0%
 
14891612.0%
 
15291312.0%
 
15325012.0%
 
ValueCountFrequency (%) 
77280012.0%
 
59520012.0%
 
49000012.0%
 
48695112.0%
 
46552812.0%
 

popularity
Real number (ℝ≥0)

Distinct34
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.04
Minimum14
Maximum82
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:04.684579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.7
Q138
median48.5
Q361.5
95-th percentile76.65
Maximum82
Range68
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation17.34753997
Coefficient of variation (CV)0.3611061608
Kurtosis-0.4710776383
Mean48.04
Median Absolute Deviation (MAD)11.5
Skewness0.01146902912
Sum2402
Variance300.9371429
MonotocityNot monotonic
2020-12-02T13:37:04.792579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%) 
5048.0%
 
6336.0%
 
3836.0%
 
4724.0%
 
1524.0%
 
3724.0%
 
3924.0%
 
4024.0%
 
7524.0%
 
4324.0%
 
Other values (24)2652.0%
 
ValueCountFrequency (%) 
1412.0%
 
1524.0%
 
2112.0%
 
2212.0%
 
2312.0%
 
ValueCountFrequency (%) 
8212.0%
 
8112.0%
 
7812.0%
 
7524.0%
 
7012.0%
 

danceability
Real number (ℝ≥0)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67396
Minimum0.188
Maximum0.901
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:04.919622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.188
5-th percentile0.43585
Q10.59225
median0.682
Q30.79625
95-th percentile0.8711
Maximum0.901
Range0.713
Interquartile range (IQR)0.204

Descriptive statistics

Standard deviation0.1533225172
Coefficient of variation (CV)0.2274949807
Kurtosis1.772820036
Mean0.67396
Median Absolute Deviation (MAD)0.1105
Skewness-1.082154763
Sum33.698
Variance0.02350779429
MonotocityNot monotonic
2020-12-02T13:37:05.044579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
0.80424.0%
 
0.77712.0%
 
0.53312.0%
 
0.90112.0%
 
0.18812.0%
 
0.76212.0%
 
0.70312.0%
 
0.79712.0%
 
0.65512.0%
 
0.79412.0%
 
Other values (39)3978.0%
 
ValueCountFrequency (%) 
0.18812.0%
 
0.22512.0%
 
0.41212.0%
 
0.46512.0%
 
0.49312.0%
 
ValueCountFrequency (%) 
0.90112.0%
 
0.88312.0%
 
0.87212.0%
 
0.8712.0%
 
0.85412.0%
 

energy
Real number (ℝ≥0)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69732
Minimum0.123
Maximum0.989
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:05.197622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.123
5-th percentile0.4153
Q10.541
median0.713
Q30.8755
95-th percentile0.9663
Maximum0.989
Range0.866
Interquartile range (IQR)0.3345

Descriptive statistics

Standard deviation0.1983068577
Coefficient of variation (CV)0.2843842965
Kurtosis-0.2476232221
Mean0.69732
Median Absolute Deviation (MAD)0.1755
Skewness-0.4240380102
Sum34.866
Variance0.0393256098
MonotocityNot monotonic
2020-12-02T13:37:05.322621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
0.49524.0%
 
0.40812.0%
 
0.83512.0%
 
0.81412.0%
 
0.40912.0%
 
0.90312.0%
 
0.66712.0%
 
0.44112.0%
 
0.95312.0%
 
0.93312.0%
 
Other values (39)3978.0%
 
ValueCountFrequency (%) 
0.12312.0%
 
0.40812.0%
 
0.40912.0%
 
0.42312.0%
 
0.44112.0%
 
ValueCountFrequency (%) 
0.98912.0%
 
0.97612.0%
 
0.96912.0%
 
0.96312.0%
 
0.95312.0%
 

key
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.42
Minimum0
Maximum11
Zeros4
Zeros (%)8.0%
Memory size400.0 B
2020-12-02T13:37:05.441577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q310
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.928389606
Coefficient of variation (CV)0.6118986926
Kurtosis-1.299745351
Mean6.42
Median Absolute Deviation (MAD)3.5
Skewness-0.4227202681
Sum321
Variance15.4322449
MonotocityNot monotonic
2020-12-02T13:37:05.536621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
111020.0%
 
7918.0%
 
1714.0%
 
10510.0%
 
948.0%
 
048.0%
 
836.0%
 
236.0%
 
624.0%
 
524.0%
 
ValueCountFrequency (%) 
048.0%
 
1714.0%
 
236.0%
 
312.0%
 
524.0%
 
ValueCountFrequency (%) 
111020.0%
 
10510.0%
 
948.0%
 
836.0%
 
7918.0%
 

loudness
Real number (ℝ)

UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.97136
Minimum-21.849
Maximum-1.737
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:05.656621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-21.849
5-th percentile-14.86535
Q1-9.8135
median-7.045
Q3-5.06975
95-th percentile-2.99055
Maximum-1.737
Range20.112
Interquartile range (IQR)4.74375

Descriptive statistics

Standard deviation4.226692849
Coefficient of variation (CV)-0.5302348469
Kurtosis1.905846027
Mean-7.97136
Median Absolute Deviation (MAD)2.251
Skewness-1.249532779
Sum-398.568
Variance17.86493244
MonotocityNot monotonic
2020-12-02T13:37:05.782621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-10.73712.0%
 
-9.94612.0%
 
-3.00112.0%
 
-8.93612.0%
 
-6.80612.0%
 
-5.62812.0%
 
-4.20512.0%
 
-4.79512.0%
 
-14.17112.0%
 
-4.17812.0%
 
Other values (40)4080.0%
 
ValueCountFrequency (%) 
-21.84912.0%
 
-19.83212.0%
 
-15.08912.0%
 
-14.59212.0%
 
-14.17112.0%
 
ValueCountFrequency (%) 
-1.73712.0%
 
-2.5712.0%
 
-2.98212.0%
 
-3.00112.0%
 
-3.4112.0%
 

mode
Boolean

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
0
28 
1
22 
ValueCountFrequency (%) 
02856.0%
 
12244.0%
 
2020-12-02T13:37:05.884622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

speechiness
Real number (ℝ≥0)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15946
Minimum0.0295
Maximum0.95
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:05.974622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0295
5-th percentile0.03638
Q10.060775
median0.1017
Q30.19525
95-th percentile0.51955
Maximum0.95
Range0.9205
Interquartile range (IQR)0.134475

Descriptive statistics

Standard deviation0.1861507936
Coefficient of variation (CV)1.167382376
Kurtosis9.31608448
Mean0.15946
Median Absolute Deviation (MAD)0.0504
Skewness2.94140512
Sum7.973
Variance0.03465211796
MonotocityNot monotonic
2020-12-02T13:37:06.107578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
0.20624.0%
 
0.21712.0%
 
0.59212.0%
 
0.081312.0%
 
0.091612.0%
 
0.12912.0%
 
0.21312.0%
 
0.06112.0%
 
0.9512.0%
 
0.098412.0%
 
Other values (39)3978.0%
 
ValueCountFrequency (%) 
0.029512.0%
 
0.033112.0%
 
0.035312.0%
 
0.037712.0%
 
0.040312.0%
 
ValueCountFrequency (%) 
0.9512.0%
 
0.84112.0%
 
0.59212.0%
 
0.43112.0%
 
0.3212.0%
 

acousticness
Real number (ℝ≥0)

UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.138713569
Minimum9.15e-06
Maximum0.774
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:06.244578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.15e-06
5-th percentile2.925e-05
Q10.013725
median0.0828
Q30.2145
95-th percentile0.42275
Maximum0.774
Range0.77399085
Interquartile range (IQR)0.200775

Descriptive statistics

Standard deviation0.1660602412
Coefficient of variation (CV)1.197144896
Kurtosis3.24659558
Mean0.138713569
Median Absolute Deviation (MAD)0.0816215
Skewness1.661373987
Sum6.93567845
Variance0.02757600369
MonotocityNot monotonic
2020-12-02T13:37:06.378621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00044712.0%
 
0.033712.0%
 
0.0055112.0%
 
0.21812.0%
 
0.078512.0%
 
0.77412.0%
 
0.087112.0%
 
0.16412.0%
 
7.46e-0512.0%
 
0.0042712.0%
 
Other values (40)4080.0%
 
ValueCountFrequency (%) 
9.15e-0612.0%
 
1.77e-0512.0%
 
2.25e-0512.0%
 
3.75e-0512.0%
 
7.46e-0512.0%
 
ValueCountFrequency (%) 
0.77412.0%
 
0.50112.0%
 
0.43412.0%
 
0.40912.0%
 
0.40512.0%
 

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2403394996
Minimum0
Maximum0.922
Zeros11
Zeros (%)22.0%
Memory size400.0 B
2020-12-02T13:37:06.516622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.34e-06
median0.002195
Q30.60625
95-th percentile0.90375
Maximum0.922
Range0.922
Interquartile range (IQR)0.60624766

Descriptive statistics

Standard deviation0.3621729203
Coefficient of variation (CV)1.50692217
Kurtosis-0.7562066982
Mean0.2403394996
Median Absolute Deviation (MAD)0.002195
Skewness1.068284588
Sum12.01697498
Variance0.1311692242
MonotocityNot monotonic
2020-12-02T13:37:06.635622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
01122.0%
 
0.90624.0%
 
0.14524.0%
 
0.0812.0%
 
0.084412.0%
 
0.92212.0%
 
6.81e-0612.0%
 
0.75512.0%
 
3.45e-0612.0%
 
0.0081712.0%
 
Other values (28)2856.0%
 
ValueCountFrequency (%) 
01122.0%
 
1.21e-0612.0%
 
1.97e-0612.0%
 
3.45e-0612.0%
 
3.64e-0612.0%
 
ValueCountFrequency (%) 
0.92212.0%
 
0.90624.0%
 
0.90112.0%
 
0.86112.0%
 
0.85712.0%
 

liveness
Real number (ℝ≥0)

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.198342
Minimum0.0618
Maximum0.98
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:06.758622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0618
5-th percentile0.064375
Q10.093675
median0.119
Q30.25925
95-th percentile0.5797
Maximum0.98
Range0.9182
Interquartile range (IQR)0.165575

Descriptive statistics

Standard deviation0.1846974593
Coefficient of variation (CV)0.9312070025
Kurtosis6.058899023
Mean0.198342
Median Absolute Deviation (MAD)0.0303
Skewness2.314119912
Sum9.9171
Variance0.03411315147
MonotocityNot monotonic
2020-12-02T13:37:06.907622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
0.10724.0%
 
0.12624.0%
 
0.088724.0%
 
0.11924.0%
 
0.090312.0%
 
0.12712.0%
 
0.49912.0%
 
0.3912.0%
 
0.412.0%
 
0.082912.0%
 
Other values (36)3672.0%
 
ValueCountFrequency (%) 
0.061812.0%
 
0.063312.0%
 
0.063712.0%
 
0.065212.0%
 
0.07412.0%
 
ValueCountFrequency (%) 
0.9812.0%
 
0.62912.0%
 
0.59512.0%
 
0.56112.0%
 
0.49912.0%
 

valence
Real number (ℝ≥0)

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.342118
Minimum0.0344
Maximum0.869
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:07.036622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0344
5-th percentile0.0544
Q10.182
median0.309
Q30.4295
95-th percentile0.7605
Maximum0.869
Range0.8346
Interquartile range (IQR)0.2475

Descriptive statistics

Standard deviation0.2087728388
Coefficient of variation (CV)0.6102363478
Kurtosis0.1709860612
Mean0.342118
Median Absolute Deviation (MAD)0.1325
Skewness0.836023712
Sum17.1059
Variance0.04358609824
MonotocityNot monotonic
2020-12-02T13:37:07.161622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%) 
0.28924.0%
 
0.27824.0%
 
0.32824.0%
 
0.31312.0%
 
0.17712.0%
 
0.12812.0%
 
0.16812.0%
 
0.56412.0%
 
0.44212.0%
 
0.56812.0%
 
Other values (37)3774.0%
 
ValueCountFrequency (%) 
0.034412.0%
 
0.037512.0%
 
0.053512.0%
 
0.055512.0%
 
0.10312.0%
 
ValueCountFrequency (%) 
0.86912.0%
 
0.82412.0%
 
0.76512.0%
 
0.75512.0%
 
0.67912.0%
 

tempo
Real number (ℝ≥0)

UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.33676
Minimum70.608
Maximum179.196
Zeros0
Zeros (%)0.0%
Memory size400.0 B
2020-12-02T13:37:07.480627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum70.608
5-th percentile91.7615
Q1107.27725
median124.0415
Q3133.603
95-th percentile155.52425
Maximum179.196
Range108.588
Interquartile range (IQR)26.32575

Descriptive statistics

Standard deviation21.80919815
Coefficient of variation (CV)0.1768264235
Kurtosis0.4142343832
Mean123.33676
Median Absolute Deviation (MAD)9.9255
Skewness0.101750149
Sum6166.838
Variance475.641124
MonotocityNot monotonic
2020-12-02T13:37:07.615621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
126.99812.0%
 
119.99612.0%
 
118.00912.0%
 
141.89412.0%
 
147.99212.0%
 
124.98812.0%
 
132.65112.0%
 
118.9712.0%
 
179.19612.0%
 
81.18512.0%
 
Other values (40)4080.0%
 
ValueCountFrequency (%) 
70.60812.0%
 
81.18512.0%
 
88.3112.0%
 
95.9812.0%
 
96.01512.0%
 
ValueCountFrequency (%) 
179.19612.0%
 
171.97712.0%
 
160.03112.0%
 
150.01612.0%
 
149.05212.0%
 

Interactions

2020-12-02T13:36:46.228777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:46.400825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:46.523821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:46.651826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:46.765821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:46.880831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.014841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.128823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.265821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.386813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.504822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.619823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.738823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.864821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:47.978821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:48.102815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:48.209788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:48.321822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:48.450822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:48.564826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:48.760860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:48.866821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:48.973822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:49.080822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:49.193826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:49.326824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:49.450822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:49.577822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:49.690827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:49.806821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:49.938827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.054812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.194821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.310776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.423814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.539823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.658778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.774822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.879821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:50.989825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.085825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.182822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.297821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.391822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.506821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.602821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.697821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.792820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:51.891821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.005825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.111828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.223821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.323825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.424824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.630821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.729821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.843821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:52.942821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.039822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.134821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.233822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.365821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.490821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.622778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.738824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.861821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:53.998822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:54.117821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:54.253821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:54.372778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:54.489821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:54.606821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:54.729779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:54.839821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:54.942821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.052826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.148821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.246822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.360812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.456821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.570823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.668821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.765821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.860818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:55.959821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:56.091811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:56.215778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:56.345821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:56.463821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:56.583821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:56.723777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:56.841824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:56.974826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:57.091821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:57.208826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:57.432824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:57.553822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:57.665821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:57.769823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:57.878821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:57.973516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.070623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.183621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.279625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.391612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.488623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.583622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.678625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.778579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.888622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:58.992622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.101622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.199621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.296622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.413624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.508622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.622626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.717622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.812623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:36:59.906621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.006627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.122621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.227667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.335621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.428625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.525624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.638621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.732579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.845625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:00.940627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.035622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.131622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.229625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.345622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.453622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.566622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.664623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.767622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.884622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:01.983624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:02.107622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:02.206621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:02.306622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:02.404624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-02T13:37:07.738624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-02T13:37:07.971621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-02T13:37:08.202625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-02T13:37:08.440577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-02T13:37:08.685622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-02T13:37:02.626627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-02T13:37:03.107621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

track_namealbumtrack_idartistdurationpopularitydanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempo
010 Feet DownPerception68biLwi894rMQPeIiSky2tNF217106630.6200.8355-6.63400.09840.0964000.0000120.26900.053596.090
1Fall Slowly (feat. Ashanti) - Extended VersionEvolution7wK4pOTZKVaAJ00rziu901Joyner Lucas292056590.5420.4536-11.20800.24000.2040000.0000070.13700.037588.310
2Let You DownPerception52okn5MNA47tk87PeZJLELNF212120820.6620.7145-5.68000.12100.3120000.0000000.17900.4640147.997
3Piece Of Your HeartPiece Of Your Heart1DFD5Fotzgn6yYXkYsKiGsMEDUZA152913810.6770.74410-6.80600.02950.0404000.0001600.07400.6310124.080
4Berlin NightsBerlin Nights6gG1R1bFdJeNc2ERAwXxCbVnce Dolanbay292115380.9010.45710-13.23800.16300.2290000.4240000.09770.5310127.999
5EvolutionEvolution2VopDw2GlF3uwD1kihHmTTJoyner Lucas153250620.6870.8199-6.67000.43100.2180000.0000000.39200.568081.185
6MOSKAUREISE, REISE4L9UGREMQBfYLmGwlACgTVRammstein256306600.4930.96311-3.41010.06380.0000230.6670000.30500.3050147.992
7Really ReallyIslah (Deluxe)10I3CmmwT0BkOVhduDy53oKevin Gates232093690.7620.6660-6.05510.09160.0042700.0000000.12700.2780118.970
8In The (Last) Moment - Robag's Ponk Pramen NB RemixIn The (Last) Moment71q5nLzXgzJO24PTZvgEA1Audiofly486951220.5670.5527-14.59200.03770.0785000.9010000.08870.1680124.003
9CreatePsychological Warfare6xuXAoOyyOTXtpE1e2j3D3Jocko Willink126417210.7030.1231-21.84910.84100.0193000.0000000.10700.2870132.651

Last rows

track_namealbumtrack_idartistdurationpopularitydanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempo
40FragileFragile3YYqctc3S1DH1i827bKpAhKora (CA)454008390.8700.55011-8.97800.06410.0061000.8200000.09450.138120.011
41Barrels of WhiskeySeven Hearts One Soul3b8arlkmuJs5xCpZQ9NxHBThe O'Reillys and the Paddyhats236226500.6510.9390-3.00110.03530.0127000.0025200.31900.824133.006
42Generation YouPorn - Agia Napa EditGefällt euch Faber so besser? EP3kwVVzIFs3kkncZhE0aXPNFaber202369300.6530.6299-8.85100.13300.1720000.0000040.12600.278128.027
43Full MetalFull Metal3oMueNQTlVgOdIfFP9ENz9BVDLVD224400390.2250.90311-7.20610.20600.0000750.1230000.56100.32599.614
44Blue (Da Ba Dee) - Luis Rodriguez RemixBlue (Da Ba Dee) Luis Rodriguez Remix4YHkUrSYE0xzUqp7noMUWDEiffel 65277734500.6260.7847-2.98200.07800.0302000.0081700.12000.376128.055
45Cyka BlyatCyka Blyat4ZjYcAWfghKtYCBzVLOyi2DJ Blyatman182408630.7940.95311-4.27900.15900.0258000.1450000.06370.679150.016
46Area 51Area 514qWCjHL1CtBV57uwdkhIXnMylky220293340.8390.6678-3.62310.12400.0004470.7410000.27200.289120.006
47Huldra - Other VersionHuldra569gNjph2g07MmjtMm6vKmGidge490000380.7720.51410-11.96100.07540.1480000.9060000.09340.103118.015
48Change of PlansChange of Plans5922vbc1Fz0Qd0AVC6w9ItRiyoon595200430.8040.4097-12.12410.06100.2200000.8570000.10900.32899.998
49Self CareSwimming5bJ1DrEM4hNCafcDd1oxHxMac Miller345133780.5200.5381-8.10910.20600.3670000.0018700.11900.177141.894